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Scalable Biclustering Algorithm Considers the Presence or Absence of Properties
Abstract
Most existing biclustering algorithms take into account the properties that hold for a set of objects. However, it could be beneficial in several application domains such as organized crimes, genetics, or digital marketing to identify homogeneous groups of similar objects in terms of both the presence and the absence of attributes. In this paper, the author proposes a scalable and efficient algorithm of biclustering that exploits a binary matrix to produce at least three types of biclusters where the cell's column (1) are filled with 1's, (2) are filled with 0's, and (3) some columns filled with 1's and/or with 0's. This procedure is scalable and it's executed without having to consider the complementary of the initial binary context. The implementation and validation of the method on data sets illustrates its potential in the discovery of relevant patterns.
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